\u201cThey want a wide field of view \u2026 so they can take in as much of the sky as they can, to pull down as much of these pixels they can to analyze, because the big view is where you end up finding interesting little nuggets to have the big boys like James Webb (space telescope) zero in on and say, \u2018Whoa. Look at that. We\u2019ve never seen that before,’\u201d Redfern said.<\/p>\n
That\u2019s where artificial intelligence comes in.<\/p>\n
Scientists are increasingly training AI systems using trusted, established data sets like those produced by the Gaia mission,<\/a> which mapped the Milky Way in extraordinary detail.<\/p>\n By feeding AI those verified observations, researchers can teach systems to recognize patterns, flag anomalies and prioritize what deserves closer attention.<\/p>\n In effect, AI is becoming a first-pass filter \u2014 separating meaningful signals from background noise in a way that would take humans far longer.<\/p>\n That approach is already being built into the next generation of space telescopes. The upcoming Nancy Grace Roman Space Telescope<\/a> at NASA\u2019s Goddard Space Flight Center in Maryland is designed not just to capture vast amounts of data, but to process and organize it efficiently from day one.<\/p>\n Engineers and scientists are working in parallel, developing both the hardware that gathers information and the systems that interpret it.<\/p>\n The goal is to avoid a scenario where groundbreaking data sits unused because it can\u2019t be processed fast enough.<\/p>\n \u201cIf you can\u2019t make sense of how to process and get stuff out of that data, it\u2019s junk,\u201d Redfern said.<\/p>\n Eyeing AI with caution<\/p>\n Researchers are aware of the risks of AI overreliance \u2014 particularly, the possibility of false positives or missed discoveries if systems are not properly trained or monitored. That\u2019s why human oversight remains a central part of the process.<\/p>\n That balance between machine efficiency and human judgment is becoming the defining challenge of modern astronomy. And it\u2019s not limited to large institutions.<\/p>\n \u201cAmateurs are even using AI to some extent. Pretty cool,\u201d Redfern said.<\/p>\n Amateur astronomers are beginning to use AI software to enhance images, identify objects and even analyze data from their own telescopes. And they stand to benefit as NASA publicly shares more observations of transient events, which allows citizen scientists to participate in real-time discovery and turn their equipment toward newly identified phenomena.<\/p>\n In the coming years, as new missions launch and observatories come online, that partnership between human curiosity and artificial intelligence is likely to define the next era of discovery \u2014 one where the challenge is no longer just seeing deeper into space, but understanding what\u2019s already in view.<\/p>\n And somewhere in that flood of data, scientists believe, are discoveries waiting to be found \u2014 signals that could reshape how we understand the universe itself.<\/p>\n Making smarter decisions far from Earth \u201cWe have been using AI for quite some time, actually, as an agency,\u201d said Bethany Theiling, a NASA Goddard researcher working on future AI applications for space exploration.<\/p>\n She pointed to the Perseverance rover\u2019s landing on Mars, when the spacecraft autonomously helped pick out the best landing area. \u201cThat was a huge moment.\u201d<\/p>\n The next frontier is giving spacecraft more ability to analyze data, prioritize discoveries and respond to unexpected findings on their own.<\/p>\n Theiling\u2019s group at Goddard is called ASTRA \u2014 Adaptive Sensing Technology for Responsive Autonomy.<\/p>\n \u201cWe\u2019re actually trying to figure out ways to do science when we can\u2019t communicate to the ground,\u201d Theiling said.<\/p>\n \u201cHow do you make decisions? How do you analyze data on board? How do we do the most amazing science when we can\u2019t actually be there?\u201d she said.<\/p>\n That could be especially important for missions to distant worlds such as Europa, one of Jupiter\u2019s moons, or Enceladus, one of Saturn\u2019s moons \u2014 both considered promising places to search for life.<\/p>\n \u201cWhat if we miss seeing life?\u201d Theiling said. \u201cWhat if it\u2019s there and we don\u2019t see it because we were just limited in what we can do?\u201d<\/p>\n She said NASA\u2019s approach is not about handing decisions to an inscrutable \u201cblack box.\u201d Her team is focused on explainable AI, so scientists can understand why a system reached a conclusion.<\/p>\n \u201cThis is no black box,\u201d Theiling said. \u201cThis is not, \u2018Oh, just trust me, we found life.\u2019\u201d<\/p>\n She also said AI could also help spacecraft and telescopes work together. If one instrument spots something unusual, such as a gamma ray burst, AI could help determine which other telescopes or researchers are best positioned to follow up quickly.<\/p>\n Theiling said large language models are not currently being used onboard spacecraft because they require too much computing power. Instead, NASA is working on smaller models that can operate within the strict limits of deep space missions.<\/p>\n \u201cWhat we\u2019re trying to do in response is create really, really lightweight models,\u201d she said. \u201cIf you\u2019re out there in space, you have a limited amount of power.\u201d<\/p>\n Before sending humans deeper into space, ask why As NASA and other space agencies look deeper into the solar system, one retired Maryland astrophysicist says the first question should not be whether humans can go farther, but why they should.<\/p>\n Beth Hufnagel, a retired astronomy and astrophysics professor from Anne Arundel Community College, said robotic probes and artificial intelligence may be better suited than humans for deep space exploration.<\/p>\n \u201cPeople are expensive, dirty and fragile,\u201d Hufnagel said, arguing that scientists should start with the question they are trying to answer before deciding whether humans need to be involved.<\/p>\n Hufnagel said missions to the moon have helped answer real scientific questions, including clues about the age of Earth. But she questioned whether current efforts to send astronauts back to the moon or eventually to Mars are driven more by competition than science.<\/p>\n She pointed to China, India, Japan and the European Union as examples of space powers leaning heavily on robotic exploration.<\/p>\n For Hufnagel, the issue is not whether space exploration matters. It is whether the mission matches the question.<\/p>\n \u201cWhat is the purpose? What is the goal?\u201d Hufnagel said.<\/p>\n Science fact, not science fiction<\/p>\n![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/05\/DSCN0016-300x225.jpg\")
The Roman Space Telescope in GSFC\u2019s huge, clean room. Flight components and the Mylar tent used to protect the telescope during testing can be seen. (Courtesy Greg Redfern)<\/p>\n![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/05\/Greg1-300x200.jpg\")
Bright Jupiter is on the right, Saturn in the Milky Way and Mars on the left. All are easily visible at Shenandoah National Park on June 2018. (WTOP\/Greg Redfern)<\/p>\n
\n![\"NASA](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/05\/NASA_Artemis_Houston_78762-scaled.jpg\")
Artifacts sit in the Apollo Mission Control room at Johnson Space Center on Thursday, July 24, 2025, in Houston. (AP Photo\/Ashley Landis)<\/p>\n
\n![\"Cosmic](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/05\/Cosmic_Butterfly_61752-1024x683.jpg\")
This image provided by NSF NOIRLab shows NGC 6302, a billowing planetary nebula that resembles a cosmic butterfly. (NSF NOIRLab via AP)<\/p>\n